The invention is directed to an improved technique for the detection of thin objects, for example, explosives along the walls of luggage. More specifically, the invention is directed to an improved technique for the detection of thin objects such as explosives using X-ray computed tomography (CT).
Detection of explosives in luggage is an extremely challenging problem because the amount of explosive required to do catastrophic damage is relatively small and because plastic explosives can be formed into almost any desired shape. Perhaps the most challenging configuration for detection is sheet explosive, where the material is deformed into a thin sheet with a very small physical extent in one direction.
One conventional way of detecting explosives is through the use of X-ray CT. X-ray CT is a technique which determines the internal make-up of an object by passing X-rays through the object and measuring the attenuation of the X-rays passing through the object. In this technique the object is sub-divided into many voxels, a voxel being the basic volumetric unit for imaging purposes. Compared with other objects in luggage, explosives have a specific range of densities, for example, 1.2 to 1.8 gm/cc, and accordingly attenuate X-rays differently than non-explosives.
In general, CT systems are designed so that the voxel size roughly corresponds to the smallest object of interest in the image. In cases where high contrast sensitivity is required, this approach is clearly justified. Indeed, voxel sizes somewhat smaller than the spatial dimension of interest are often used. However, this approach greatly increases system cost and complexity because it requires a large number of detector elements, view angle positions, and voxels for image acquisition and reconstruction. X-ray source loading is also significantly increased due to the need to maintain roughly the same number of X-rays and hence the same signal-to-noise ratio for the smaller voxel dimensions.
If the thin dimension of the sheet of explosive is smaller than the linear voxel dimension in a CT image, the measured density of a voxel of interest decreases due to the fact that the voxel is not completely filled with explosive. FIGS. 1 and 2 illustrate this problem for a configuration with an explosive density p of 1.5 gm/cc. FIG. 1 shows a voxel V.sub.1 completely filled with explosive, wherein the average density of the voxel is 1.5 gm/cc. FIG. 2 shows a voxel V.sub.2 containing a section of sheet explosive, where the thickness of the sheet is 20% of the voxel linear dimension. The average density .rho. in the voxel V.sub.2 is reduced to 0.3 gm/cc. Conventional CT systems would compute a density for voxel V.sub.2 which is less than the density expected for an explosive and thus would not identify voxel V.sub.2 as containing an explosive.
The challenge is to discriminate such a sheet explosive from background material in the suitcase.